Bayesian Inference for Nonnegative Matrix Factor Deconvolution Models

Serap Kirbiz, Ali Taylan Cemgil, Bilge Günsel

Abstract: In this paper we develop a probabilistic interpretation and a full Bayesian inference for non-negative matrix deconvolution (NMFD) model. Our ultimate goal is unsupervised extraction of multiple sound objects from a single channel auditory scene. The proposed method facilitates automatic model selection and determination of the sparsity criteria. Our approach retains attractive features of standard NMFD based methods such as fast convergence and easy implementation. We demonstrate the use of this algorithm in the log-frequency magnitude spectrum domain, where we employ it to perform model order selection and control sparseness directly.